From e993fe13af329cd45e61490c60a5c28389c4cb08 Mon Sep 17 00:00:00 2001
From: Gael varoquaux <gael.varoquaux@normalesup.org>
Date: Wed, 1 Dec 2010 19:59:24 +0100
Subject: [PATCH] ENH: Add control of the dtype in img_to_graph
---
scikits/learn/feature_extraction/image.py | 7 +++++--
1 file changed, 5 insertions(+), 2 deletions(-)
diff --git a/scikits/learn/feature_extraction/image.py b/scikits/learn/feature_extraction/image.py
index 4303781b6b..027e631e18 100644
--- a/scikits/learn/feature_extraction/image.py
+++ b/scikits/learn/feature_extraction/image.py
@@ -62,7 +62,7 @@ def _mask_edges_weights(mask, edges, weights):
def img_to_graph(img, mask=None,
- return_as=sparse.coo_matrix):
+ return_as=sparse.coo_matrix, dtype=np.float):
""" Create a graph of the pixel-to-pixel connections with the
gradient of the image as a the edge value.
@@ -75,6 +75,8 @@ def img_to_graph(img, mask=None,
pixels.
return_as: np.ndarray or a sparse matrix class, optional
The class to use to build the returned adjacency matrix.
+ dtype: dtype, optional
+ The data of the returned sparse matrix
"""
img = np.atleast_3d(img)
n_x, n_y, n_z = img.shape
@@ -92,7 +94,8 @@ def img_to_graph(img, mask=None,
graph = sparse.coo_matrix((np.hstack((weights, weights, img)),
(np.hstack((i_idx, diag_idx)),
np.hstack((j_idx, diag_idx)))),
- (n_voxels, n_voxels))
+ (n_voxels, n_voxels),
+ dtype=dtype)
if return_as is np.ndarray:
return graph.todense()
return return_as(graph)
--
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